A major obstacle in single-cell sequencing is given by sample contamination
with foreign DNA. To guarantee clean samples and to prevent the introduction
of contamination in novel species into public databases, considerable quality
control efforts are put into post-sequencing analysis. Contamination screening
usually relies on reference-based methods such as database alignment or marker
gene search which limits the set of detectable contaminants to known species.
But as the majority of species is unknown, a particular challenge is given by
the detection of de-novo structure which requires screening techniques that
can operate reference-free.

Acdc is a tool specifically developed to aid the quality control process. By
combining supervised and unsupervised methods, it reliably detects both known
and de-novo contaminants. First, 16S gene prediction and the inclusion of
ultrafast exact alignment techniques enable sequence classification using
existing knowledge from databases. Second, reference-free inspection is
enabled by the use of state-of-the-art machine learning techniques that
include fast, non-linear dimensionality reduction of oligonucleotide
signatures and subsequent clustering algorithms that automatically estimate
the number of clusters. The latter also enables to remove any contaminants,
ending up with a clean sample without re-sequencing. Furthermore, given the
data complexity and the ill-posedness of clustering, acdc employs
bootstrapping techniques to provide statistically profound confidence values.
Tested on a large number of samples from diverse sequencing projects, our
software is able to quickly and accurately identify contamination. Results are
displayed in an interactive result interface. Acdc can be run from the web as
well as a dedicated command line application, which allows easy integration
into large sequencing projects.

Acdc can reliably detect contamination in single-cell sequencing. In addition
to database-driven detection, it complements existing tools by its
unsupervised techniques, which allow for the detection of de-novo
contaminants, too. As quality control is currently done manually, this
contribution bears the potential of drastically reducing the amount of
resources put into these processes, particularly in the context of limited
availability of reference species, e.g. in de-novo analysis.